Speech AI use cases for Learning Management Systems

Learn how LMS platforms can use speech-to-text and audio intelligence models to make online learning better.

Speech AI use cases for Learning Management Systems

A learning management system (LMS) is an online platform that stores, manages, and delivers educational content. K-12 school systems, universities, and businesses alike use learning management systems to teach and train students and employees.

However, an LMS is more than a basic interface for educational content. It can also be an ecosystem of educators, learners, and innovative technology that elevates learning opportunities.

Artificial Intelligence like Speech AI is part of that ecosystem more and more: AI can automate repetitive tasks, help predict student outcomes, and generate educational content.

The global AI in education market size was valued at $1.82 billion in 2021, according to Grand View Research. By 2030, their research projects the market size to grow at a compound annual growth rate (CAGR) of 36%.

How can businesses use AI for education successfully and responsibly? Platforms and tools will need to offer seamless integration of features that complement teachers’ expertise, provide students with the opportunity to forge connections, and offer all users back more time.

What is Speech AI?

Speech AI is a subfield of Artificial Intelligence that uses Machine and Deep Learning to understand, analyze, and communicate using human language.

One subset of Speech AI is Automatic Speech Recognition (ASR), or Speech-to-Text, which converts audio data into text. High-quality ASR models quickly generate transcriptions with near human-level accuracy. They can also include features like speaker recognition, automatic punctuation and casing, and language detection.

Speech AI also includes Audio Intelligence models, which analyze and draw insights from audio data. These models can perform tasks like summarization, identifying topics, and PII redaction.

Large Language Models (LLMs), another component of Speech AI, are powerful AI models that have a robust understanding of general-purpose language and communication. They are made even more accessible through LLM frameworks like LeMUR, which allow companies to easily build Generative AI audio analysis tools on top of spoken data.

5 benefits of Speech AI for LMS platforms

Learning management systems comprise a variety of functions and tools for users of all kinds. 

Some of those include:

  • Content management and storage
  • eLearning delivery
  • Learning and development (L&D) analysis
  • Role management and access
  • Assessment and feedback tools
  • Security and data privacy

Whether you represent an LMS looking to add functions to your platform in-house, or a software developer building applications for LMS integration, here are five ways your business can integrate Speech AI to create innovative learning solutions.

1. Guarantee accessible course materials

Speech AI to achieve this: Speech-to-Text, Real-Time Transcription, Speaker Diarization, Automatic Punctuation and Casing, Language Detection

One of online learning’s strengths is the opportunity to provide content in alternative formats—from videos and animations to VR and AR simulations. However, every format needs to be accessible to every learner.

For schools and universities, add-ons like video transcripts and closed captioning are not additional features, but basic rights to which all learners are entitled. 

In order for schools to be ADA compliant, those with disabilities must be able to “acquire the same information, engage in the same interactions and enjoy the same services” as those without, according to the Office of Civil Rights and the U.S. Department of Education. 

A speech-to-text model that transcribes pre-recorded files can automatically generate high-quality transcripts, subtitles, and closed captioning files to accompany all video and audio content stored in an LMS.

Many ASR models are also powerful enough to recognize individual speakers, automatically detect casing and punctuation, remove filler words, and more.

With real-time speech-to-text models, LMS developers can also generate live captions using real-time streaming. Companies that use AssemblyAI’s API can provide captions within a few hundred milliseconds that are updated as the speaker continues and the model gains more context.

Platforms can also integrate these tools for learner-generated content. In-platform speech-to-text could help learners of all abilities submit assignments, take notes, and engage in live chat during class with others.

2. Catalog course content more effectively

Speech AI to achieve this: Speech-to-Text, Audio Intelligence, Topic Detection, Key Phrases

For an LMS that houses a content library meant for user exploration, Topic Detection could be used to enhance search. A basic search system might match queries to course titles or descriptions. 

Instead, developers could use Speech-to-Text and Audio Intelligence to identify the different topics discussed in course videos and include those topics as tags in the course metadata. With this integration, search results won’t be limited by the information relayed in titles.

These same principles could be used to test additional search filters and options. What additional transcript data could make course catalogs more searchable? For example, could you use a Key Phrases model to help learners find videos by previewing the most interesting or important moments from a course?

Automatically generated highlights could also be useful for marketing the LMS catalog, sharing your product with potential partners, and adding demo content to your site.

3. Help educators evaluate learners’ reading comprehension

Speech AI to achieve this: Speech-to-Text, Word Timings, Confidence Scores, Filler Words, International Language Support

When educators can harness the power of audio, it expands on traditional methods of assessment. For example, literacy assessments are an evaluation of a student’s ability to read that helps diagnose skill gaps and monitor their progress. 

Typically, the student reads the indicated text, while the teacher listens and manually tracks their performance on indicators such as pronunciation, letter knowledge, and comprehension.

This process can be time-intensive and requires educators to multitask—simultaneously balancing active listening, outcome tracking, and student support.

Literably leveraged Speech AI to create a new approach for K-8 students, one that provides expertly-scored literacy assessments while maintaining a human-centric approach. Audio recordings of students reading are sent to Literably, which uses speech recognition technology and expert graders to return scores within 24 hours.

High-quality ASR is also capable of recognizing speakers’ accents, an especially important aspect for educators working with ESOL (English Speakers of Other Languages) students. Any interpretation of their literacy should be equitable.

An LMS that incorporates Speech AI-assisted literacy assessments could be used for distance learning, as well as in-classroom LMS support.

4. Build a feedback loop between platforms, educators and learners

Speech AI to achieve this: Speech-to-Text, Audio Intelligence, Customized LeMUR Tasks, Sentiment Analysis, LeMUR Action Items

Feedback is an integral part of learning—it’s the primary tool that students and teachers alike use to measure their progress and effectiveness. These can be grades, comments on an assignment, or student reviews throughout the term, to name a few.

LMS and app developers can use Speech AI throughout their system to build a better feedback loop. For example, an LMS could integrate the LeMUR framework into the educators’ UI that takes custom requests for feedback on uploaded video lessons. How could the lecture be more concise? What are some ways to encourage student engagement throughout?

Tools within the LMS could also provide the option for educators to leave audio feedback on student assignments. A survey of undergraduate students who received voice comments on written assignments showed that about two-thirds preferred the addition of voice comments rather than written feedback alone.

Voice comments can make it easier to capture details and nuance that written comments do not. Speech-to-Text can be used to keep voice comments accessible and convenient for students.

Product teams could push this feedback loop further and offer additional analysis features using Audio Intelligence and LeMUR. For example, a Sentiment Analysis model could analyze feedback and let students know if they’re overall positive, negative, or neutral. The LeMUR framework could be used to compile action items and suggestions within audio comments for learners, too.

5. Equip learners with smart study tools

Speech AI to achieve this: Speech-to-Text, Audio Intelligence, Summarization, Auto Chapters, Topic Detection, Key Phrases, Customized LeMUR Tasks

Integrated Audio Intelligence features could help students review material and study for upcoming assignments. For example, models like Summarization or Auto Chapters could be implemented to make it easier for students to search for key points or segments of interest.

Speech-to-Text options could also let students record audio versions of their notes or study plans that they can read through later. 

Further development can lead to smarter studying: How can you integrate Audio Intelligence and the LeMUR framework into an app that helps students annotate those study transcripts? Topic Detection and Key Phrases could be used in tandem to automatically link relevant course videos, or even specific timestamps, throughout their notes.

Other development avenues could lead to automatically generated study guides based on video content from the course. Using the LeMUR framework, businesses could give users the option to submit prompts to this kind of app for custom study guides.